1. Field of the Invention
This invention relates to the dimensionalization of media and in particular to system and methods for converting media or multimedia between a first lower dimensional representation and a second higher dimensional representation.
2. Background
Developing image segmentation-classification-recognition systems is challenging. Traditional cameras, microscopes, and imaging sensors are able to acquire two-dimensional (2D) images that lack the depth information. These 2D images greatly limit our ability to perceive and understand the complexity of real-world objects, as the 2D images don't have the necessary levels of detail of objects, shapes, and texture.
One example of the problem with 2D images is in the area of fingerprint-based identification. Fingerprint-based identification of an unknown victim, witness, or suspect requires that the fingerprint obtained at a crime scene match a pre-recorded fingerprint template stored in the on-board fingerprint database acquired by using different scanners. Fingerprint identification has been a great challenge due to its complex database search, which can often require the query of hundreds of millions of entries. In practice, a search of such databases involves the use of keywords such as gender and age to reduce the search space. However, the reduced database is still large and, in many cases, such keyword information is not available. Another common strategy is to divide the fingerprint database into a number of bins, based on some predefined classes such as global fingerprint patterns. Still, the state-of-the-art classifiers perform at an error rate of about 6% for these predefined classes.
In can be appreciated that that more advanced systems with small and inexpensive fingerprint capture devices, fast computing hardware, recognition rate and speed would significantly boost the crime-fighting capabilities of law. In addition, the use of multiple fingers and 3D imaging techniques could enhance recognition reliability. Fingerprint identification systems may also be extended to other applications including but not limited to computer network logon, electronic data security, e-commerce, internet access, credit card, physical access control, cellular phones, medical records, distance learning, etc. national id card, driver's license, social security, welfare disbursement, border and passport control, corpse identification, criminal investigation, terrorist identification, and parenthood determination.
Another example of an area in 2D is in barcode technology. Barcode technology continues to increase in demand with constantly improving digital technologies, such as cell phones and webcams. Traditional or 1D (one dimensional) barcodes, which represent data in monochrome parallel lines, offer a simple, inexpensive, and accurate method of encoding information. However, traditional symbologies are only capable of encoding at most a couple digits in a barcode, and in today's Information Age companies need to encode hundreds to thousands of characters to accommodate applications such as the labeling of semiconductor packages, credit cards, and software media.
Many variations of traditional barcodes have been created to provide larger amounts of information. 2D barcodes, which can be broadly classified as either stacked or matrix codes, differ from their 1D counterpart in that they are not “vertically redundant” and store essential data in both length and height. Though vertical redundancy allows a 2D symbology to store more information, it decreases the barcode's readability, especially when damaged or scanned from a distance. Since 2D code is sensitive to printing variations, it also requires additional data capacity to prevent misreads and provide a satisfactory read rate, often by encoding extra data for error correction. 3D barcodes, which are read by using differences in height, rather than contrast, to distinguish between bars and spaces using a special reader, are also used though are more expensive and not as widespread as their 1D and 2D counterparts.
Alternatives to black and white barcodes have also been proposed, namely in color barcodes. Color barcodes not only hold aesthetic value, but also store more information in the same physical size of the code. Colors have been used to represent data such as a manufacturer's code, delivery and expirations dates, and vendor identification. Examples of color symbologies include Microsoft's High Capacity Color Barcode (HCCB) and Image ID's Color Barcode System. The former, for instance, uses colored triangles instead of black and white lines or squares to increase information density. However, in increasing information content, color barcodes, like their monochrome multidimensional counterparts, have also increased complexity of recognition algorithms as they require greater image segmentation. As a result, color barcodes are not prevalent and are primarily limited to identifying commercial audiovisual works such as motion pictures, video games, broadcasts, digital video recordings and other media.
The primary criterion for judging the aforementioned barcodes is their performance. Barcode performance is usually evaluated by three basic criteria: reliability, density, first time read rate. To improve performance, barcodes often contain error detection and correction check-digits, which help ensure accurate readings. Checking operations, however, can be very expensive in computer time and may increase barcode overhead costs. In addition, there are two more criteria at issue, i.e. security and cost. Barcode security is defined as either a protection of barcodes against unauthorized access or a technique for ensuring that barcoded information cannot be read or compromised by any individuals without authorization. Barcodes are often not traceable to a specific source of production and therefore do not necessarily provide a means of authentication that the product identified has been legitimately produced.
Error correction for barcodes has primarily come in the form of single level error correction. The use of current technologies for error detecting and correcting are often insufficient or complex, requiring additional extensive hardware and reducing other performance criteria such as speed and information handling capability. Resolving the problems with current error correction capabilities may require new barcodes more resistant to damage and with perhaps double the error correction capabilities. Although many barcode readers can read multiple barcode symbologies, there is no symbology that can be read by different kinds of readers at different data density levels.
Attempts to prevent barcodes from being reproduced have also been made. Text printed near barcodes in ink only visible under ultraviolet light and microscopic size particles (taggents) that electromagnetic energy in a unique and quantifiable manner when read are examples of attempts to provide barcode authentication. However, these devices can be copied and tend to add expense to a product.
The problems illustrated in the above fingerprinting and barcoding examples can also be extended to many other fields, including but not limited to: film, currency, medicine, bio-technology, insurance, biochemistry, botany, brain mapping, cell biology, DNA, dentistry, orthopedics, developmental biology, forensics, fluid inclusions, energy, textile industry, food technology, wide range of materials, consumer, security, military, defense, computer vision, fracture analysis, geology and micro fossils, immunolabeling, inspection, in situ hybridization, marine biology, morphology, micromanipulation, neuroscience, pathology, paleontology, parisitology, physiology, plant biology, quality control, research, semiconductor, tissue engineering, photography, education, electronic games and others.
Existing 2D-to-3D conversion technologies can be classified into following 2 basic classes: stereo conversion methods and reconstruction algorithms based on sequences/slices of images. However, these methods of 2D-to-3D conversion are not yet sufficient for the more demanding applications in biology, cosmetics, fabric and food industry, medicine, materials, military, defense, electronic games and others. The restrictions of 2D signals greatly limit our ability to perceive and to understand the complexity of real-world objects and raise issues of how to create a cost efficient, automatic, higher dimension model from a single lower one.